mirror of
https://github.com/Karaka-Management/phpOMS.git
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152 lines
3.9 KiB
PHP
152 lines
3.9 KiB
PHP
<?php
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/**
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* Orange Management
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*
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* PHP Version 7.4
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*
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* @package phpOMS\Ai\Ocr
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* @copyright Dennis Eichhorn
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* @license OMS License 1.0
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* @version 1.0.0
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* @link https://orange-management.org
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*/
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declare(strict_types=1);
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namespace phpOMS\Ai\Ocr;
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use phpOMS\Math\Topology\MetricsND;
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/**
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* Matching a value with a set of coins
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*
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* @package phpOMS\Ai\Ocr
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* @license OMS License 1.0
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* @link https://orange-management.org
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* @since 1.0.0
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*/
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final class BasicOcr
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{
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private array $Xtrain = [];
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private array $ytrain = [];
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public function trainWith(string $dataPath, string $labelPath) : void
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{
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$Xtrain = $this->readImages($dataPath);
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$ytrain = $this->readLabels($labelPath);
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$this->Xtrain = \array_merge($this->Xtrain, $this->extractFeatures($Xtrain));
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$this->ytrain = \array_merge($this->ytrain, $this->extractFeatures($ytrain));
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}
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private function readImages(string $path) : array
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{
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$fp = \fopen($path, 'r');
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$magicNumber = \unpack('l', \fread($fp, 4))[1];
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$numberOfImages = \unpack('l', \fread($fp, 4))[1];
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$numberOfRows = \unpack('l', \fread($fp, 4))[1];
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$numberOfColumns = \unpack('l', \fread($fp, 4))[1];
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$images = [];
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for ($i = 0; $i < $numberOfImages; ++$i) {
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$image = [];
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for ($row = 0; $row < $numberOfRows; ++$row) {
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$rows = [];
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for ($col = 0; $col < $numberOfColumns; ++$col) {
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$rows[] = \unpack('l', \fread($fp, 1))[1]; //fread($fp, 1);
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}
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$image[] = $rows;
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}
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$images[] = $image;
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}
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return $images;
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}
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private function readLabels(string $path) : array
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{
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$fp = \fopen($path, 'r');
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$magicNumber = \unpack('l', \fread($fp, 4))[1];
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$numberOfLabels = \unpack('l', \fread($fp, 4))[1];
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$labels = [];
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for ($i = 0; $i < $numberOfLabels; ++$i) {
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$labels[] = \unpack('l', \fread($fp, 1))[1]; //fread($fp, 1);
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}
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return $labels;
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}
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public function flatten(array $data) : array
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{
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$flat = [];
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foreach ($data as $sublist) {
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foreach ($sublist as $pixel) {
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$flat[] = $pixel;
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}
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}
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return $flat;
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}
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private function extractFeatures(array $data) : array
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{
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$features = [];
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foreach ($data as $sample) {
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$features[] = $this->flatten($sample);
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}
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return $features;
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}
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private function kNearest(array $Xtrain, array $ytrain, array $Xtest, int $k = 3) : array
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{
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$predictedLabels = [];
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foreach ($Xtest as $sample) {
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// @todo: consider to path the k-limit to the getDistances function for earlier filtering
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$distances = $this->getDistances($Xtrain, $sample);
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\asort($distances);
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// find possible k-labels for a image
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$kKeys = \array_keys(\array_slice($distances, 0, $k));
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$candidateLabels = [];
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foreach ($kKeys as $key) {
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$candidateLabels[] = $ytrain[$key];
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}
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// find best match
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$countedCandidates = \array_count_values($candidateLabels);
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\asort($countedCandidates);
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$predictedLabels[] = ['label' => \array_key_first($countedCandidates), 'prob' => \reset($countedCandidates) / $k];
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}
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return $predictedLabels;
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}
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private function getDistances(array $Xtrain, $sample) : array
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{
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$dist = [];
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foreach ($Xtrain as $train) {
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$dist[] = MetricsND::euclidean($train, $sample);
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}
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return $dist;
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}
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public function match(string $path) : array
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{
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// @todo: implement image reading if it isn't an mnist file
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$Xtest = $this->readImages($path);
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return $this->kNearest($this->Xtrain, $this->ytrain, $Xtest);
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}
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}
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